Technical Field
The invention relates to user experiences. More particularly, the invention relates to offering services to users based on predictive user models.
Description of the Background Art
Identifying customer preferences and requirements is must for the success of any customer service organization. One way to do this is through application of a user profile. A user profile is a collection of personal data associated with a specific user. A user profile can also be considered as representation of a user model. This information can be exploited by customer service organizations by taking into account the users' characteristics and preferences. Basically, a user profile is a collection of user related data which may reflect interests and preferences of a user. Each user account has an associated profile. A user profile may be created by manually obtaining inputs from the user or else by automatically obtaining preferences of the user related to any particular service.
Keeping in mind the users' preferences based on the user profile information, existing systems react to a user's queries about any services, information, etc. that the user needs. In addition to this, such systems can also select and offer services related to the user's preferences. Further, advertisers and/or content providers can offer targeted products, incentives, or enticements suitable to each user's profile.
User profile information, such as demographic, geographic, personality, areas of interest, people, hobbies, etc. may be used in addition to other information, such as keywords or categories which are associated with a request to select a guide. Search results, an advertisement, a search resource, a previous query, etc. may be selected based on profile information. The user profile or preference data specific to a user is automatically detected, extracted, and stored. In some cases, user profile or preference data is stored in a server independently of the device used by a user to communicate. In some cases, storage of the user profile and/or preference data is split across the device used by a user and a server independently of the device. The next time the user communicates from any device to the same domain or application to exchange information, the stored user profile or preference data is retrieved and used during the communication. Most existing systems only react to a subset of user's needs based on the user's profile information and the user's preferences information. Some systems try to predict user's intent and needs by tracking the user behavior and interactions with systems.
In March 2006, the Advertising Research Foundation announced the first definition of customer engagement the first definition of CE at the re:think! 52nd Annual ARF Convention and Expo: “Engagement is turning on a prospect to a brand idea enhanced by the surrounding context.” Customer engagement can also refer to the stages consumers travel through as they interact with a particular brand. This customer engagement cycle, or customer journey, has been described using a myriad of terms but most often consists of five different stages: awareness, consideration, inquiry, purchase and retention. Marketers employ connection strategy to speak to would-be customers at each stage, with media that addresses their particular needs and interests. When conducting search engine marketing and search engine optimization, or placing advertisements, marketers must devise media and/or keywords and phrases that encourage customer flow through the customer engagement cycle, towards purchase. Existing definitions of customer journeys are focused on individual transactions. Further, state of the art customer journey mapping mechanisms do not involve the prediction of services based on the obtained data and the accessing of data across multiple channels available for the customers. In such cases, the user must follow up manually to get status and upgrades of the service to which he has been subscribed. This may require additional effort from the user and may also cause inconvenience to the user.
Further, existing systems for profiling and/or building of customer databases offer services requested by the user and are not capable for initiating service offerings by analyzing user preferences automatically. Hence, such existing systems are only capable of reacting to the requests and are not capable of proactively delivering services and resolutions based on predicted user needs and interests.